clr {utiml} | R Documentation |
Calibrated Label Ranking (CLR) for multi-label Classification
Description
Create a CLR model for multilabel classification.
Usage
clr(
mdata,
base.algorithm = getOption("utiml.base.algorithm", "SVM"),
...,
cores = getOption("utiml.cores", 1),
seed = getOption("utiml.seed", NA)
)
Arguments
mdata |
A mldr dataset used to train the binary models. |
base.algorithm |
A string with the name of the base algorithm. (Default:
|
... |
Others arguments passed to the base algorithm for all subproblems |
cores |
The number of cores to parallelize the training. Values higher
than 1 require the parallel package. (Default:
|
seed |
An optional integer used to set the seed. This is useful when
the method is run in parallel. (Default: |
Details
CLR is an extension of label ranking that incorporates the calibrated scenario. The introduction of an artificial calibration label, separates the relevant from the irrelevant labels.
Value
An object of class RPCmodel
containing the set of fitted
models, including:
- labels
A vector with the label names.
- rpcmodel
A RPC model.
- brmodel
A BR model used to calibrated the labels.
References
Brinker, K., Furnkranz, J., & Hullermeier, E. (2006). A unified model for multilabel classification and ranking. In Proceeding of the ECAI 2006: 17th European Conference on Artificial Intelligence. p. 489-493. Furnkranz, J., Hullermeier, E., Loza Mencia, E., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine Learning, 73(2), 133-153.
See Also
Other Transformation methods:
brplus()
,
br()
,
cc()
,
dbr()
,
ebr()
,
ecc()
,
eps()
,
esl()
,
homer()
,
lift()
,
lp()
,
mbr()
,
ns()
,
ppt()
,
prudent()
,
ps()
,
rakel()
,
rdbr()
,
rpc()
Other Pairwise methods:
rpc()
Examples
model <- clr(toyml, "RANDOM")
pred <- predict(model, toyml)